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Despite the abundance of epidemiological evidence for the high comorbid rate between psoriasis and obesity, systematic approaches on common in ammatory mechanisms have not been adequately explored. We performed a meta-analysis of publicly available RNA-sequencing datasets to unveil putative mechanisms that are postulated to exacerbate both diseases, utilizing both late-stage, disease-speci c meta-analyses and consensus gene co-expression network (cWGCNA). Single-gene meta-analyses reported several common in ammatory mechanisms fostered by the perturbed expression pro le of pathogenic cell types. Assessment of gene overlaps between both diseases revealed signi cant overlaps between up-(n = 170, P-value = 6.07×10 -65 ) and down-regulated (n = 49, P-value = 7.1×10 − 7 ) genes, associated with increased T cell response and activated transcription factors. Our cWGCNA approach disentangled 48 consensus modules, associated with either the differentiation of leukocytes or metabolic pathways with similar correlation signals in both diseases. Notably, all our analyses con rmed the association of the perturbed T helper (Th)17 differentiation pathway in both diseases. Our novel ndings through whole transcriptomic analyses characterize the in ammatory commonalities between psoriasis and obesity implying the assessment of several expression pro les that could serve as putative comorbid disease progression biomarkers and therapeutic interventions.
Despite the abundance of epidemiological evidence for the high comorbid rate between psoriasis and obesity, systematic approaches on common in ammatory mechanisms have not been adequately explored. We performed a meta-analysis of publicly available RNA-sequencing datasets to unveil putative mechanisms that are postulated to exacerbate both diseases, utilizing both late-stage, disease-speci c meta-analyses and consensus gene co-expression network (cWGCNA). Single-gene meta-analyses reported several common in ammatory mechanisms fostered by the perturbed expression pro le of pathogenic cell types. Assessment of gene overlaps between both diseases revealed signi cant overlaps between up-(n = 170, P-value = 6.07×10 -65 ) and down-regulated (n = 49, P-value = 7.1×10 − 7 ) genes, associated with increased T cell response and activated transcription factors. Our cWGCNA approach disentangled 48 consensus modules, associated with either the differentiation of leukocytes or metabolic pathways with similar correlation signals in both diseases. Notably, all our analyses con rmed the association of the perturbed T helper (Th)17 differentiation pathway in both diseases. Our novel ndings through whole transcriptomic analyses characterize the in ammatory commonalities between psoriasis and obesity implying the assessment of several expression pro les that could serve as putative comorbid disease progression biomarkers and therapeutic interventions.
Background Air pollution and genetic risk have been found to contribute to both onset and development of psoriasis. However, the extent to which genetic susceptibility modifies the effects of air pollutants on the risk of incident psoriasis remains unknown. Objectives Our study aimed to assess the association between joint exposure to multiple air pollutants and the risk of psoriasis and the modification by the genetic susceptibility. Methods This prospective study included 451,064 participants with complete air pollution data and free of psoriasis at baseline from the UK Biobank. All participants were enrolled from 2006 to 2010 and followed up to 2022. The air pollution score (APS) was calculated to assess the joint exposure to multiple air pollutants, including particulate matter (PM) with diameters ≤ 2.5 μm (PM2.5), between 2.5 and 10 μm (PM2.5−10), and ≤ 10 μm (PM10), as well as nitrogen dioxide (NO2) and nitrogen oxides (NOx). To evaluate the genetic risk, the polygenic risk score (PRS) for psoriasis was constructed. The Cox proportional hazard models were used to assess the association of air pollution and genetic susceptibility with the risk of psoriasis. Stratified analyses were conducted based on the individual characteristics. Results During a median follow-up of 13.79 years, 4414 psoriasis events were recorded. The hazard ratios (HRs) [95% confidence intervals (CIs)] for psoriasis were 1.036 (0.936-1.147), 1.091 (0.987-1.206), 1.159 (1.048-1.283), and 1.163 (1.052-1.286) in higher quintile groups compared with the lowest quintile of APS (P trend <0.05). When considering genetic susceptibility, participants with high PRS and high APS had the greatest risk of incident psoriasis [HR (95% CI): 1.962 (1.630-2.362)] than those with low PRS and low APS. The HRs (95% CIs) for PM2.5-10, NOx, PM2.5 absorbance, PM2.5, NO2, and PM10 in the group with high exposure level and genetic risk were 1.831 (1.537-2.181), 1.722 (1.431-2.073), 1.698 (1.416-2.037), 1.619 (1.353-1.938), 1.504 (1.252-1.806), and 1.425 (1.192-1.704), respectively. Conclusions Long-term exposure to various air pollutants is positively associated with an increased risk of incident psoriasis, particularly in individuals with high genetic risk. More comprehensive measures are needed to reduce the air pollution levels for better prevention of psoriasis.
Genome-wide association studies (GWASs) for atopic dermatitis (AD) have uncovered 81 risk loci in European participants, however translating these findings into functional and therapeutic insights remains challenging. We conducted a transcriptome-wide association study (TWAS) in AD leveraging cis-eQTL data from 3 central AD tissues and the latest GWAS of AD in Europeans. We implemented the OTTERS pipeline that combines polygenic risk score (PRS) techniques accommodating diverse assumptions in the architecture of gene regulation. We also used differential expression datasets and co-expression networks to characterize the transcriptomic landscape of AD. We identified 176 gene-tissue associations covering 126 unique genes (53 novel). Most TWAS risk genes were identified by adaptive PRS frameworks, with non-significant differences compared to clumping and thresholding approaches. The novel TWAS risk genes were enriched in allergic reactions (e.g., AQP7, AFF4), skin barrier integrity (e.g., ACER3) and inflammatory pathways (e.g., TAPBPL). By integrating co-expression networks of lesional AD skin, we identified 16 hub genes previously identified as TWAS risk genes (6 novel) that orchestrate inflammatory responses (e.g., HSPA4) and keratinization (e.g., LCE3E, LCE3D), serving as potential drug targets through drug-gene interactions. Collectively, our findings provide additional risk genes for AD with potential implications in therapeutic approaches.
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